MKD8: An Enhanced YOLOv8 Model for High-Precision Weed Detection

Wenxuan Su, Wenzhong Yang (), Jiajia Wang, Doudou Ren and Danny Chen
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Wenxuan Su: School of Computer Science and Technology, Xinjiang University, Urumqi 830046, China
Wenzhong Yang: School of Computer Science and Technology, Xinjiang University, Urumqi 830046, China
Jiajia Wang: School of Computer Science and Technology, Xinjiang University, Urumqi 830046, China
Doudou Ren: School of Computer Science and Technology, Xinjiang University, Urumqi 830046, China
Danny Chen: School of Computer Science and Technology, Xinjiang University, Urumqi 830046, China

Agriculture, 2025, vol. 15, issue 8, 1-23

Abstract: Weeds are an inevitable element in agricultural production, and their significant negative impacts on crop growth make weed detection a crucial task in precision agriculture. The diversity of weed species and the substantial background noise in weed images pose considerable challenges for weed detection. To address these challenges, constructing a high-quality dataset and designing an effective artificial intelligence model are essential solutions. We captured 2002 images containing 10 types of weeds from cotton and corn fields, establishing the CornCottonWeed dataset, which provides rich data support for weed-detection tasks. Based on this dataset, we developed the MKD8 model for weed detection. To enhance the model’s feature extraction capabilities, we designed the CVM and CKN modules, which effectively alleviate the issues of deep-feature information loss and the difficulty in capturing fine-grained features, enabling the model to more accurately distinguish between different weed species. To suppress the interference of background noise, we designed the ASDW module, which combines dynamic convolution and attention mechanisms to further improve the model’s ability to differentiate and detect weeds. Experimental results show that the MKD8 model achieved mAP 50 and mAP [50:95] of 88.6% and 78.4%, respectively, on the CornCottonWeed dataset, representing improvements of 9.9% and 8.5% over the baseline model. On the public weed dataset CottoWeedDet12, the mAP 50 and mAP [50:95] reached 95.3% and 90.5%, respectively, representing improvements of 1.0% and 1.4% over the baseline model.

Keywords: precision agriculture; weed dataset; YOLOv8; object detection (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
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